Overview

Dataset statistics

Number of variables12
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory93.9 KiB
Average record size in memory96.1 B

Variable types

Numeric6
Categorical6

Reproduction

Analysis started2023-08-19 09:10:24.129493
Analysis finished2023-08-19 09:10:29.647447
Duration5.52 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct46
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.702
Minimum20
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-19T14:40:29.750915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile22
Q131
median42
Q354
95-th percentile64
Maximum65
Range45
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.266771
Coefficient of variation (CV)0.31068265
Kurtosis-1.2175133
Mean42.702
Median Absolute Deviation (MAD)12
Skewness0.037307478
Sum42702
Variance176.0072
MonotonicityNot monotonic
2023-08-19T14:40:29.902905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
64 31
 
3.1%
28 28
 
2.8%
54 28
 
2.8%
49 27
 
2.7%
34 27
 
2.7%
29 27
 
2.7%
40 26
 
2.6%
45 25
 
2.5%
26 25
 
2.5%
36 25
 
2.5%
Other values (36) 731
73.1%
ValueCountFrequency (%)
20 18
1.8%
21 17
1.7%
22 17
1.7%
23 16
1.6%
24 23
2.3%
25 24
2.4%
26 25
2.5%
27 22
2.2%
28 28
2.8%
29 27
2.7%
ValueCountFrequency (%)
65 20
2.0%
64 31
3.1%
63 24
2.4%
62 22
2.2%
61 23
2.3%
60 25
2.5%
59 22
2.2%
58 18
1.8%
57 17
1.7%
56 22
2.2%

Gender
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Male
501 
Female
499 

Length

Max length6
Median length4
Mean length4.998
Min length4

Characters and Unicode

Total characters4998
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 501
50.1%
Female 499
49.9%

Length

2023-08-19T14:40:30.026535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-19T14:40:30.139461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
male 501
50.1%
female 499
49.9%

Most occurring characters

ValueCountFrequency (%)
e 1499
30.0%
a 1000
20.0%
l 1000
20.0%
M 501
 
10.0%
F 499
 
10.0%
m 499
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3998
80.0%
Uppercase Letter 1000
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1499
37.5%
a 1000
25.0%
l 1000
25.0%
m 499
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
M 501
50.1%
F 499
49.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 4998
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1499
30.0%
a 1000
20.0%
l 1000
20.0%
M 501
 
10.0%
F 499
 
10.0%
m 499
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4998
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1499
30.0%
a 1000
20.0%
l 1000
20.0%
M 501
 
10.0%
F 499
 
10.0%
m 499
 
10.0%

Marital Status
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Married
350 
Divorced
326 
Single
324 

Length

Max length8
Median length7
Mean length7.002
Min length6

Characters and Unicode

Total characters7002
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowMarried
3rd rowSingle
4th rowMarried
5th rowMarried

Common Values

ValueCountFrequency (%)
Married 350
35.0%
Divorced 326
32.6%
Single 324
32.4%

Length

2023-08-19T14:40:30.233403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-19T14:40:30.353786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
married 350
35.0%
divorced 326
32.6%
single 324
32.4%

Most occurring characters

ValueCountFrequency (%)
r 1026
14.7%
i 1000
14.3%
e 1000
14.3%
d 676
9.7%
M 350
 
5.0%
a 350
 
5.0%
D 326
 
4.7%
v 326
 
4.7%
o 326
 
4.7%
c 326
 
4.7%
Other values (4) 1296
18.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6002
85.7%
Uppercase Letter 1000
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1026
17.1%
i 1000
16.7%
e 1000
16.7%
d 676
11.3%
a 350
 
5.8%
v 326
 
5.4%
o 326
 
5.4%
c 326
 
5.4%
n 324
 
5.4%
g 324
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
M 350
35.0%
D 326
32.6%
S 324
32.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 7002
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1026
14.7%
i 1000
14.3%
e 1000
14.3%
d 676
9.7%
M 350
 
5.0%
a 350
 
5.0%
D 326
 
4.7%
v 326
 
4.7%
o 326
 
4.7%
c 326
 
4.7%
Other values (4) 1296
18.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7002
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1026
14.7%
i 1000
14.3%
e 1000
14.3%
d 676
9.7%
M 350
 
5.0%
a 350
 
5.0%
D 326
 
4.7%
v 326
 
4.7%
o 326
 
4.7%
c 326
 
4.7%
Other values (4) 1296
18.5%

Education Level
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Master
262 
High School
250 
PhD
245 
Bachelor
243 

Length

Max length11
Median length8
Mean length7.001
Min length3

Characters and Unicode

Total characters7001
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaster
2nd rowHigh School
3rd rowMaster
4th rowPhD
5th rowBachelor

Common Values

ValueCountFrequency (%)
Master 262
26.2%
High School 250
25.0%
PhD 245
24.5%
Bachelor 243
24.3%

Length

2023-08-19T14:40:30.457408image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-19T14:40:30.586897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
master 262
21.0%
high 250
20.0%
school 250
20.0%
phd 245
19.6%
bachelor 243
19.4%

Most occurring characters

ValueCountFrequency (%)
h 988
14.1%
o 743
 
10.6%
a 505
 
7.2%
e 505
 
7.2%
r 505
 
7.2%
l 493
 
7.0%
c 493
 
7.0%
M 262
 
3.7%
t 262
 
3.7%
s 262
 
3.7%
Other values (8) 1983
28.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5256
75.1%
Uppercase Letter 1495
 
21.4%
Space Separator 250
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h 988
18.8%
o 743
14.1%
a 505
9.6%
e 505
9.6%
r 505
9.6%
l 493
9.4%
c 493
9.4%
t 262
 
5.0%
s 262
 
5.0%
g 250
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
M 262
17.5%
S 250
16.7%
H 250
16.7%
P 245
16.4%
D 245
16.4%
B 243
16.3%
Space Separator
ValueCountFrequency (%)
250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6751
96.4%
Common 250
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
h 988
14.6%
o 743
11.0%
a 505
 
7.5%
e 505
 
7.5%
r 505
 
7.5%
l 493
 
7.3%
c 493
 
7.3%
M 262
 
3.9%
t 262
 
3.9%
s 262
 
3.9%
Other values (7) 1733
25.7%
Common
ValueCountFrequency (%)
250
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
h 988
14.1%
o 743
 
10.6%
a 505
 
7.2%
e 505
 
7.2%
r 505
 
7.2%
l 493
 
7.0%
c 493
 
7.0%
M 262
 
3.7%
t 262
 
3.7%
s 262
 
3.7%
Other values (8) 1983
28.3%
Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Self-Employed
347 
Employed
328 
Unemployed
325 

Length

Max length13
Median length10
Mean length10.385
Min length8

Characters and Unicode

Total characters10385
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEmployed
2nd rowUnemployed
3rd rowEmployed
4th rowUnemployed
5th rowSelf-Employed

Common Values

ValueCountFrequency (%)
Self-Employed 347
34.7%
Employed 328
32.8%
Unemployed 325
32.5%

Length

2023-08-19T14:40:30.697527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-19T14:40:30.811826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
self-employed 347
34.7%
employed 328
32.8%
unemployed 325
32.5%

Most occurring characters

ValueCountFrequency (%)
e 1672
16.1%
l 1347
13.0%
m 1000
9.6%
p 1000
9.6%
o 1000
9.6%
y 1000
9.6%
d 1000
9.6%
E 675
6.5%
S 347
 
3.3%
f 347
 
3.3%
Other values (3) 997
9.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8691
83.7%
Uppercase Letter 1347
 
13.0%
Dash Punctuation 347
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1672
19.2%
l 1347
15.5%
m 1000
11.5%
p 1000
11.5%
o 1000
11.5%
y 1000
11.5%
d 1000
11.5%
f 347
 
4.0%
n 325
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
E 675
50.1%
S 347
25.8%
U 325
24.1%
Dash Punctuation
ValueCountFrequency (%)
- 347
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10038
96.7%
Common 347
 
3.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1672
16.7%
l 1347
13.4%
m 1000
10.0%
p 1000
10.0%
o 1000
10.0%
y 1000
10.0%
d 1000
10.0%
E 675
6.7%
S 347
 
3.5%
f 347
 
3.5%
Other values (2) 650
 
6.5%
Common
ValueCountFrequency (%)
- 347
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10385
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1672
16.1%
l 1347
13.0%
m 1000
9.6%
p 1000
9.6%
o 1000
9.6%
y 1000
9.6%
d 1000
9.6%
E 675
6.5%
S 347
 
3.3%
f 347
 
3.3%
Other values (3) 997
9.6%

Credit Utilization Ratio
Real number (ℝ)

Distinct101
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50995
Minimum0
Maximum1
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-19T14:40:30.931172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.06
Q10.25
median0.53
Q30.75
95-th percentile0.96
Maximum1
Range1
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.29105672
Coefficient of variation (CV)0.5707554
Kurtosis-1.2031434
Mean0.50995
Median Absolute Deviation (MAD)0.24
Skewness-0.04789134
Sum509.95
Variance0.084714012
MonotonicityNot monotonic
2023-08-19T14:40:31.080042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.74 24
 
2.4%
0.66 18
 
1.8%
0.96 17
 
1.7%
0.6 17
 
1.7%
0.08 16
 
1.6%
0.45 16
 
1.6%
0.22 15
 
1.5%
0.78 15
 
1.5%
0.71 15
 
1.5%
0.91 15
 
1.5%
Other values (91) 832
83.2%
ValueCountFrequency (%)
0 3
 
0.3%
0.01 9
0.9%
0.02 7
0.7%
0.03 6
 
0.6%
0.04 15
1.5%
0.05 9
0.9%
0.06 13
1.3%
0.07 7
0.7%
0.08 16
1.6%
0.09 5
 
0.5%
ValueCountFrequency (%)
1 9
0.9%
0.99 8
0.8%
0.98 12
1.2%
0.97 12
1.2%
0.96 17
1.7%
0.95 14
1.4%
0.94 7
0.7%
0.93 11
1.1%
0.92 9
0.9%
0.91 15
1.5%

Payment History
Real number (ℝ)

Distinct101
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1452.814
Minimum0
Maximum2857
Zeros10
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-19T14:40:31.216531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile142
Q1763.75
median1428
Q32142
95-th percentile2742
Maximum2857
Range2857
Interquartile range (IQR)1378.25

Descriptive statistics

Standard deviation827.93415
Coefficient of variation (CV)0.5698831
Kurtosis-1.1841781
Mean1452.814
Median Absolute Deviation (MAD)714
Skewness0.0083047057
Sum1452814
Variance685474.95
MonotonicityNot monotonic
2023-08-19T14:40:31.339960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2628 17
 
1.7%
2800 17
 
1.7%
2085 17
 
1.7%
514 16
 
1.6%
857 15
 
1.5%
1371 15
 
1.5%
1142 15
 
1.5%
1714 14
 
1.4%
485 14
 
1.4%
2571 14
 
1.4%
Other values (91) 846
84.6%
ValueCountFrequency (%)
0 10
1.0%
28 5
 
0.5%
57 6
0.6%
85 9
0.9%
114 14
1.4%
142 11
1.1%
171 5
 
0.5%
200 9
0.9%
228 7
0.7%
257 8
0.8%
ValueCountFrequency (%)
2857 12
1.2%
2828 7
0.7%
2800 17
1.7%
2771 10
1.0%
2742 9
0.9%
2714 10
1.0%
2685 9
0.9%
2657 11
1.1%
2628 17
1.7%
2600 6
 
0.6%

Number of Credit Accounts
Real number (ℝ)

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.58
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-19T14:40:31.469428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9336336
Coefficient of variation (CV)0.52574079
Kurtosis-1.2516581
Mean5.58
Median Absolute Deviation (MAD)3
Skewness-0.052238532
Sum5580
Variance8.6062062
MonotonicityNot monotonic
2023-08-19T14:40:31.562047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8 117
11.7%
10 116
11.6%
1 113
11.3%
7 106
10.6%
3 102
10.2%
4 98
9.8%
5 89
8.9%
9 88
8.8%
6 88
8.8%
2 83
8.3%
ValueCountFrequency (%)
1 113
11.3%
2 83
8.3%
3 102
10.2%
4 98
9.8%
5 89
8.9%
6 88
8.8%
7 106
10.6%
8 117
11.7%
9 88
8.8%
10 116
11.6%
ValueCountFrequency (%)
10 116
11.6%
9 88
8.8%
8 117
11.7%
7 106
10.6%
6 88
8.8%
5 89
8.9%
4 98
9.8%
3 102
10.2%
2 83
8.3%
1 113
11.3%

Loan Amount
Real number (ℝ)

Distinct897
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2471401
Minimum108000
Maximum4996000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-19T14:40:31.679530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum108000
5-th percentile329000
Q11298000
median2437500
Q33653250
95-th percentile4634550
Maximum4996000
Range4888000
Interquartile range (IQR)2355250

Descriptive statistics

Standard deviation1387046.7
Coefficient of variation (CV)0.56123902
Kurtosis-1.1585383
Mean2471401
Median Absolute Deviation (MAD)1162000
Skewness0.040700646
Sum2.471401 × 109
Variance1.9238985 × 1012
MonotonicityNot monotonic
2023-08-19T14:40:31.825665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
149000 3
 
0.3%
2604000 3
 
0.3%
2684000 3
 
0.3%
2283000 3
 
0.3%
2862000 3
 
0.3%
2081000 3
 
0.3%
3266000 3
 
0.3%
2759000 3
 
0.3%
1209000 2
 
0.2%
2169000 2
 
0.2%
Other values (887) 972
97.2%
ValueCountFrequency (%)
108000 1
 
0.1%
109000 1
 
0.1%
118000 1
 
0.1%
127000 1
 
0.1%
134000 1
 
0.1%
135000 1
 
0.1%
143000 1
 
0.1%
149000 3
0.3%
151000 1
 
0.1%
155000 1
 
0.1%
ValueCountFrequency (%)
4996000 1
0.1%
4988000 1
0.1%
4970000 1
0.1%
4963000 1
0.1%
4959000 1
0.1%
4956000 1
0.1%
4948000 1
0.1%
4946000 1
0.1%
4933000 1
0.1%
4932000 1
0.1%

Interest Rate
Real number (ℝ)

Distinct774
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.6866
Minimum1.01
Maximum19.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-19T14:40:31.965728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.01
5-th percentile2.1685
Q16.0225
median10.705
Q315.44
95-th percentile19.12
Maximum19.99
Range18.98
Interquartile range (IQR)9.4175

Descriptive statistics

Standard deviation5.4790577
Coefficient of variation (CV)0.51270355
Kurtosis-1.1851318
Mean10.6866
Median Absolute Deviation (MAD)4.72
Skewness-0.020068822
Sum10686.6
Variance30.020074
MonotonicityNot monotonic
2023-08-19T14:40:32.167548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.47 4
 
0.4%
4.38 4
 
0.4%
6.03 4
 
0.4%
4.35 4
 
0.4%
9.05 4
 
0.4%
15.28 4
 
0.4%
15.58 3
 
0.3%
5.19 3
 
0.3%
13.96 3
 
0.3%
11.39 3
 
0.3%
Other values (764) 964
96.4%
ValueCountFrequency (%)
1.01 1
0.1%
1.06 1
0.1%
1.07 1
0.1%
1.1 1
0.1%
1.11 1
0.1%
1.12 2
0.2%
1.13 1
0.1%
1.14 2
0.2%
1.15 1
0.1%
1.17 1
0.1%
ValueCountFrequency (%)
19.99 1
 
0.1%
19.98 1
 
0.1%
19.96 1
 
0.1%
19.93 1
 
0.1%
19.92 2
0.2%
19.88 1
 
0.1%
19.86 3
0.3%
19.85 2
0.2%
19.83 1
 
0.1%
19.82 1
 
0.1%

Loan Term
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
60
234 
48
205 
12
199 
36
181 
24
181 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2000
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48
2nd row60
3rd row12
4th row60
5th row36

Common Values

ValueCountFrequency (%)
60 234
23.4%
48 205
20.5%
12 199
19.9%
36 181
18.1%
24 181
18.1%

Length

2023-08-19T14:40:32.345668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-19T14:40:32.547524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
60 234
23.4%
48 205
20.5%
12 199
19.9%
36 181
18.1%
24 181
18.1%

Most occurring characters

ValueCountFrequency (%)
6 415
20.8%
4 386
19.3%
2 380
19.0%
0 234
11.7%
8 205
10.2%
1 199
10.0%
3 181
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 415
20.8%
4 386
19.3%
2 380
19.0%
0 234
11.7%
8 205
10.2%
1 199
10.0%
3 181
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 415
20.8%
4 386
19.3%
2 380
19.0%
0 234
11.7%
8 205
10.2%
1 199
10.0%
3 181
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 415
20.8%
4 386
19.3%
2 380
19.0%
0 234
11.7%
8 205
10.2%
1 199
10.0%
3 181
9.0%

Type of Loan
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Auto Loan
348 
Home Loan
328 
Personal Loan
324 

Length

Max length13
Median length9
Mean length10.296
Min length9

Characters and Unicode

Total characters10296
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPersonal Loan
2nd rowAuto Loan
3rd rowAuto Loan
4th rowAuto Loan
5th rowPersonal Loan

Common Values

ValueCountFrequency (%)
Auto Loan 348
34.8%
Home Loan 328
32.8%
Personal Loan 324
32.4%

Length

2023-08-19T14:40:32.711525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-19T14:40:32.995103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
loan 1000
50.0%
auto 348
 
17.4%
home 328
 
16.4%
personal 324
 
16.2%

Most occurring characters

ValueCountFrequency (%)
o 2000
19.4%
a 1324
12.9%
n 1324
12.9%
1000
9.7%
L 1000
9.7%
e 652
 
6.3%
A 348
 
3.4%
u 348
 
3.4%
t 348
 
3.4%
H 328
 
3.2%
Other values (5) 1624
15.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7296
70.9%
Uppercase Letter 2000
 
19.4%
Space Separator 1000
 
9.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 2000
27.4%
a 1324
18.1%
n 1324
18.1%
e 652
 
8.9%
u 348
 
4.8%
t 348
 
4.8%
m 328
 
4.5%
r 324
 
4.4%
s 324
 
4.4%
l 324
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
L 1000
50.0%
A 348
 
17.4%
H 328
 
16.4%
P 324
 
16.2%
Space Separator
ValueCountFrequency (%)
1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9296
90.3%
Common 1000
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 2000
21.5%
a 1324
14.2%
n 1324
14.2%
L 1000
10.8%
e 652
 
7.0%
A 348
 
3.7%
u 348
 
3.7%
t 348
 
3.7%
H 328
 
3.5%
m 328
 
3.5%
Other values (4) 1296
13.9%
Common
ValueCountFrequency (%)
1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10296
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 2000
19.4%
a 1324
12.9%
n 1324
12.9%
1000
9.7%
L 1000
9.7%
e 652
 
6.3%
A 348
 
3.4%
u 348
 
3.4%
t 348
 
3.4%
H 328
 
3.2%
Other values (5) 1624
15.8%

Interactions

2023-08-19T14:40:28.503025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:24.709962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:25.478725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:26.211489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:26.932540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:27.715973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:28.625902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:24.815602image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:25.591116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:26.320475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:27.063397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:27.828724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:28.748808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:24.944030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:25.711292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:26.440627image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:27.198464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:27.953584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:28.872618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:25.082997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:25.835323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:26.554882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:27.330039image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:28.085878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:28.995087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:25.215912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:25.956083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:26.668686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:27.459506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:28.253783image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:29.141154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:25.349041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:26.077962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:26.785477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:27.588667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-19T14:40:28.379968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-08-19T14:40:33.134937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
AgeCredit Utilization RatioPayment HistoryNumber of Credit AccountsLoan AmountInterest RateGenderMarital StatusEducation LevelEmployment StatusLoan TermType of Loan
Age1.0000.000-0.003-0.0490.0350.0320.0000.0000.0340.0560.0450.070
Credit Utilization Ratio0.0001.0000.004-0.001-0.0120.0480.0000.0830.0100.0000.0000.000
Payment History-0.0030.0041.0000.024-0.020-0.0160.0000.0560.0000.0000.0410.056
Number of Credit Accounts-0.049-0.0010.0241.0000.032-0.0020.0000.0000.0000.0230.0000.033
Loan Amount0.035-0.012-0.0200.0321.0000.0470.0420.0180.0000.0000.0000.000
Interest Rate0.0320.048-0.016-0.0020.0471.0000.0000.0000.0000.0170.0240.000
Gender0.0000.0000.0000.0000.0420.0001.0000.0000.0410.0000.0360.000
Marital Status0.0000.0830.0560.0000.0180.0000.0001.0000.0000.0000.0340.000
Education Level0.0340.0100.0000.0000.0000.0000.0410.0001.0000.0000.0190.000
Employment Status0.0560.0000.0000.0230.0000.0170.0000.0000.0001.0000.0290.000
Loan Term0.0450.0000.0410.0000.0000.0240.0360.0340.0190.0291.0000.000
Type of Loan0.0700.0000.0560.0330.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-08-19T14:40:29.332920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-19T14:40:29.551638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeGenderMarital StatusEducation LevelEmployment StatusCredit Utilization RatioPayment HistoryNumber of Credit AccountsLoan AmountInterest RateLoan TermType of Loan
060MaleMarriedMasterEmployed0.222685.0246750002.6548Personal Loan
125MaleMarriedHigh SchoolUnemployed0.202371.0936190005.1960Auto Loan
230FemaleSingleMasterEmployed0.222771.069570002.7612Auto Loan
358FemaleMarriedPhDUnemployed0.121371.0247310006.5760Auto Loan
432MaleMarriedBachelorSelf-Employed0.99828.0232890006.2836Personal Loan
542MaleDivorcedMasterUnemployed0.942342.02153600011.1524Personal Loan
644FemaleDivorcedBachelorUnemployed0.322800.01202100016.6136Auto Loan
724MaleDivorcedMasterEmployed0.661428.0812980006.0324Home Loan
854FemaleDivorcedPhDUnemployed0.40800.03437400010.3812Personal Loan
960MaleDivorcedPhDUnemployed0.061371.010402600011.0560Personal Loan
AgeGenderMarital StatusEducation LevelEmployment StatusCredit Utilization RatioPayment HistoryNumber of Credit AccountsLoan AmountInterest RateLoan TermType of Loan
99048FemaleSingleHigh SchoolSelf-Employed0.56257.06416100012.5048Auto Loan
99156MaleDivorcedBachelorSelf-Employed0.672428.03228300016.1236Auto Loan
99225FemaleSingleBachelorUnemployed0.492800.04160900017.6512Personal Loan
99346FemaleDivorcedPhDUnemployed0.10114.0730100014.1112Personal Loan
99453FemaleSingleMasterEmployed0.402028.0425610003.2636Home Loan
99559MaleDivorcedHigh SchoolEmployed0.741285.08353000012.9948Auto Loan
99664MaleDivorcedBachelorUnemployed0.771857.02137700018.0260Home Loan
99763FemaleSingleMasterSelf-Employed0.182628.010244300018.9512Personal Loan
99851FemaleMarriedPhDSelf-Employed0.321142.0313010001.8024Auto Loan
99937FemaleMarriedMasterSelf-Employed0.171028.0541820009.3424Auto Loan